Looking at a scene from two or more points of view, nearby objects seem to move and far away objects do not. This is one example of parallax. It is used to triangulate the distance to any object, even those not recognized. Two or more perspectives can be generated by one camera using consecutive frames, like in the moving landscape above, or by using two or more cameras to capture multiple angles simultaneously, which creates the effect of freezing 3D objects while they are in motion.
Parallax-based depth sensing has been confirmed across many species as a powerful technique. Computers can do the math faster and more accurately than any living thing. Yet parallax is not sufficient, because some objects look the same from different angles or reflect light in misleading ways.
Parallax and semantic cues are complementary. That's why nature relies on them both.
Walls are often smooth and flat. Given two images of a wall, parallax-based techniques used to understand depth in a scene can fail because most points look the same. But dogs and humans aren't confused because they know how walls work due to their semantic understanding of the scene.
Given a single image, a computer can mistake shadows for objects and brake suddenly. But two views are enough to tell that the road is flat, thanks to parallax.